A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

Abstract

Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issuescollection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling.

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

“…In contrast, remote sensing-based approaches have become popular due to their unique characteristics in data collection and presentation; that is, multitemporal remote sensing images not only reveal spatial variability, spatial distributions, and patterns of forests but also provide the potential to estimate their changes over time [4][5][6][7]. A large number of research papers on biomass estimation using remote sensing data have been published in the past three decades, as summarized in previous literature review papers (e.g., [4,6,[11][12][13][14][15][16][17]). …”

“…The optical imagery-based technologies are commonly used for biomass estimation due to high correlations between spectral bands and biomass [4,6,[17][18][19][20][21][22][23][24][25][26][27][28]. In particular, Landsat images have been the most widely used for forest aboveground biomass (AGB) estimation in the past three decades [5,6,20,24,26,[28][29][30][31][32][33][34][35][36], mainly because they are freely downloadable, have a long history, and have medium spatial resolution. The studies deal with different climate zones and forest ecosystems, from tropical to subtropical, temperate, and boreal forests [4][5][6][7][12][13][14][15]20,28,32,[37][38][39][40][41][42][43].…”

“…Estimating and mapping forest biomass/carbon stocks become essential for greenhouse gas inventories, global carbon cycle, and climate change modeling [1][2][3]. Various methods such as process models and remote sensing-based approaches have been developed and used [4][5][6][7]. The process models-based methods do not generate spatially explicit predictions and often lead to a large amount of uncertainty for specific sites, partly because too many variables and input parameters are required to run the models and partly because different source data such as climate and soil data have very coarse spatial resolutions [8][9][10].…”

“…Various remotely sensed images from optical sensors and radar and lidar systems have been utilized to estimate and map forest biomass/carbon by combining with ground sample plot data [5,6,[11][12][13][14][15]. The optical imagery-based technologies are commonly used for biomass estimation due to high correlations between spectral bands and biomass [4,6,[17][18][19][20][21][22][23][24][25][26][27][28].…”

Texture Analysis on Image Motif of Endek Bali using K-Nearest Neighbor Classification Method

“…GLCM can be calculated using the multiple spatial offset direction is 0 degrees, 45 degrees, 90 degrees, and 135 degrees. GLCM matrix obtained, characteristic texture of imagery used in this study can be measured by using the following features [14][15][16]: Contrast, is used to measure the intensity distribution fluctuation by using equation 3…”